Adversarial　examples　(AEs),　which　are　maliciously　hand-crafted　by　adding　perturbations　to　benign　images,　reveal　the　vulnerability　of　deep　neural　networks　(DNNs)　and　have　been　used　as　a　benchmark　for　evaluating　model　robustness.　With　great　efforts　have　been　devoted　to　generating　AEs　with　stronger　attack　ability,　the　visual　quality　of　AEs　is　generally　neglected　in　previous　studies.　The　lack　of　a　good　quality　measure　of　AEs　makes　it　very　hard　to　compare　the　relative　merits　of　attack　techniques　and　is　hindering　technological　advancement.　How　to　evaluate　the　visual　quality　of　AEs　remains　an　understudied　and　unsolved　problem.　In　this　work,　we　make　the　first　attempt　to　fill　the　gap　by　presenting　an　image　quality　assessment　method　specifically　designed　for　AEs.　Towards　this　goal,　we　first　construct　a　new　database,　called　AdvDB,　developed　on　diverse　adversarial　examples　with　elaborated　annotations.　We　also　propose　a　detection-based　structural　similarity　index　(AdvDSS)　for　adversarial　example　perceptual　quality　assessment.　Specifically,　the　visual　saliency　for　capturing　the　near-threshold　adversarial　distortions　is　first　detected　via　human　visual　system　(HVS)　techniques　and　then　the　structural　similarity　is　extracted　to　predict　the　quality　score.　Moreover,　we　further　propose　AEQA　for　overall　adversarial　example　quality　assessment　by　integrating　the　perceptual　quality　and　attack　intensity　of　AEs.　Extensive　experiments　validate　that　the　proposed　AdvDSS　achieves　state-of-the-art　performance　which　is　more　consistent　with　human　opinions.　©　2024　ACM.